Sentiment-Topic Modelling in Text Mining

Peer reviewed

A framework for modelling mobile radio access networks for intelligent fault management

Postprint

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Occupant location prediction in smart buildings using association rule mining

Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. We present an approach to occupant location prediction based on association rule mining, allowing prediction based on historical occupant locations. Association rule mining is a machine learning technique designed to find any correlations which exist in a given dataset. Occupant location datasets have a number of properties which differentiate them from the market basket datasets that association rule mining was originally designed for. This thesis adapts the approach to suit such datasets, focusing the rule mining process on patterns which are useful for location prediction. This approach, named OccApriori, allows for the prediction of occupants’ next locations as well as their locations further in the future, and can take into account any available data, for example the day of the week, the recent movements of the occupant, and timetable data. By integrating an existing extension of association rule mining into the approach, it is able to make predictions based on general classes of locations as well as specific locations.

Inventory of SINOPS project data sets

During the SINOPS project, an optimal state of the art simulation of the marine silicon cycle is attempted employing a biogeochemical ocean general circulation model (BOGCM) through three particular time steps relevant for global (paleo-) climate. In order to tune the model optimally, results of the simulations are compared to a comprehensive data set of 'real' observations. SINOPS' scientific data management ensures that data structure becomes homogeneous throughout the project. Practical work routine comprises systematic progress from data acquisition, through preparation, processing, quality check and archiving, up to the presentation of data to the scientific community. Meta-information and analytical data are mapped by an n-dimensional catalogue in order to itemize the analytical value and to serve as an unambiguous identifier. In practice, data management is carried out by means of the online-accessible information system PANGAEA, which offers a tool set comprising a data warehouse, Graphical Information System (GIS), 2-D plot, cross-section plot, etc. and whose multidimensional data model promotes scientific data mining. Besides scientific and technical aspects, this alliance between scientific project team and data management crew serves to integrate the participants and allows them to gain mutual respect and appreciation.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains.

Fast classification in incrementally growing spaces

[EN]The classification speed of state-of-the-art classifiers such as SVM is an important aspect to be considered for emerging applications and domains such as data mining and human-computer interaction. Usually, a test-time speed increase in SVMs is achieved by somehow reducing the number of support vectors, which allows a faster evaluation of the decision function. In this paper a novel approach is described for fast classification in a PCA+SVM scenario. In the proposed approach, classification of an unseen sample is performed incrementally in increasingly larger feature spaces. As soon as the classification confidence is above a threshold the process stops and the class label is retrieved...

Modelling and Mining To Manage

In this talk, I will describe various computational modelling and data mining solutions that form the basis of how the office of Deputy Head of Department (Resources) works to serve you. These include lessons I learn about, and from, optimisation issues in resource allocation, uncertainty analysis on league tables, modelling the process of winning external grants, and lessons we learn from student satisfaction surveys, some of which I have attempted to inject into our planning processes.

High-performance parallel systems for data-intensive computing

Thesis (Ph.D.)--University of Washington, 2016-08

Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.

Analysis and Visualisation of Crowd-sourced Tourism Data

C3S2E '16 Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering

Minería de datos con Oracle Data Miner

La intención del proyecto es mostrar las diferentes características que ofrece Oracle en el campo de la minería de datos, con la finalidad de saber si puede ser una plataforma apta para la investigación y la educación en la universidad. En la primera parte del proyecto se estudia la aplicación “Oracle Data Miner” y como, mediante un flujo de trabajo visual e intuitivo, pueden aplicarse las distintas técnicas de minería (clasificación, regresión, clustering y asociación). Para mostrar la ejecución de estas técnicas se han usado dataset procedentes de la universidad de Irvine. Con ello se ha conseguido observar el comportamiento de los distintos algoritmos en situaciones reales. Para cada técnica se expone como evaluar su fiabilidad y como interpretar los resultados que se obtienen a partir de su aplicación. También se muestra la aplicación de las técnicas mediante el uso del lenguaje PL/SQL. Gracias a ello podemos integrar la minería de datos en nuestras aplicaciones de manera sencilla. En la segunda parte del proyecto, se ha elaborado un prototipo de una aplicación que utiliza la minería de datos, en concreto la clasificación para obtener el diagnóstico y la probabilidad de que un tumor de mama sea maligno o benigno, a partir de los resultados de una citología.

Utilização de inteligência competitiva, da gestão de riscos e da computação aplicada para ganhos de competitividade em instituição organizadora de concursos

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.

Fictitious capital and the elusive quest in understanding its implications : illusions and paradoxes

This paper deals with the interaction between fictitious capital and the neoliberal model of growth and distribution, inspired by the classical economic tradition. Our renewed interest in this literature has a close connection with the recent international crisis in the capitalist economy. However, this discussion takes as its point of departure the fact that standard economic theory teaches that financial capital, in this world of increasing globalization, leads to new investment opportunities which improve levels of growth, employment, income distribution, and equilibrium. Accordingly, it is said that such financial resources expand the welfare of people and countries worldwide. Here we examine some illusions and paradoxes of such a paradigm. We show some theoretical and empirical consequences of this vision, which are quite different and have harmful constraints.